Change-centric improvement of team collaboration

In software development, teamwork is essential to the successful delivery of a final product. The software industry has historically built software utilizing development teams that share the workplace. Process models, tools, and methodologies have been enhanced to support the development of software in a collocated setting. However, since the dawn of the 21st century, this scenario has begun to change: an increasing number of software companies are adopting global software development to cut costs and speed up the development process. Global software development introduces several challenges for the creation of quality software, from the adaptation of current methods, tools, techniques, etc., to new challenges imposed by the distributed setting, including physical and cultural distance between teams, communication problems, and coordination breakdowns. A particular challenge for distributed teams is the maintenance of a level of collaboration naturally present in collocated teams. Collaboration in this situation naturally d r ops due to low awareness of the activity of the team. Awareness is intrinsic to a collocated team, being obtained through human interaction such as informal conversation or meetings. For a distributed team, however, geographical distance and a subsequent lack of human interaction negatively impact this awareness. This dissertation focuses on the improvement of collaboration, especially within geographically dispersed teams. Our thesis is that by modeling the evolution of a software system in terms of fine-grained changes, we can produce a detailed history that may be leveraged to help developers collaborate. To validate this claim, we first c r eate a model to accurately represent the evolution of a system as sequences of fine- grained changes. We proceed to build a tool infrastructure able to capture and store fine-grained changes for both immediate and later use. Upon this foundation, we devise and evaluate a number of applications for our work with two distinct goals: 1. To assist developers with real-time information about the activity of the team. These applications aim to improve developers’ awareness of team member activity that can impact their work. We propose visualizations to notify developers of ongoing change activity, as well as a new technique for detecting and informing developers about potential emerging conflicts. 2. To help developers satisfy their needs for information related to the evolution of the software system. These applications aim to exploit the detailed change history generated by our approach in order to help developers find answers to questions arising during their work. To this end, we present two new measurements of code expertise, and a novel approach to replaying past changes according to user-defined criteria. We evaluate the approach and applications by adopting appropriate empirical methods for each case. A total of two case studies – one controlled experiment, and one qualitative user study – are reported. The results provide evidence that applications leveraging a fine-grained change history of a software system can effectively help developers collaborate in a distributed setting

Supporting Modern Code Review

Modern code review is a lightweight and asynchronous process of auditing code changes that is done by a reviewer other than the author of the changes. Code review is widely used in both open source and industrial projects because of its diverse benefits, which include defect identification, code improvement, and knowledge transfer. This thesis presents three research results on code review. First, we conduct a large-scale developer survey. The objective of the survey is to understand how developers conduct code review and what difficulties they face in the process. We also reproduce the survey questions from the previous studies to broaden the base of empirical knowledge in the code review research community. Second, we investigate in depth the coding conventions applied during code review. These coding conventions guide developers to write source code in a consistent format. We determine how many coding convention violations are introduced, removed, and addressed, based on comments left by reviewers. The results show that developers put a great deal of effort into checking for convention violations, although various convention checking tools are available. Third, we propose a technique that automatically recommends related code review requests. When a new patch is submitted for code review, our technique recommends previous code review requests that contain a patch similar to the new one. Developers can locate meaningful information and development context from our recommendations. With two empirical studies and an automation technique for recommending related code reviews, this thesis broadens the empirical knowledge base for code review practitioners and provides a useful approach that supports developers in streamlining their review efforts